Reference Cluster Normalization Improves Detection of Frontotemporal Lobar Degeneration by Means of FDG-PET.

Positron emission tomography with [18F] fluorodeoxyglucose (FDG-PET) plays a well-established role in assisting early detection of frontotemporal lobar degeneration (FTLD). Here, we examined the impact of intensity normalization to different reference areas on accuracy of FDG-PET to discriminate between patients with mild FTLD and healthy elderly subjects. FDG-PET was conducted at two centers using different acquisition protocols: 41 FTLD patients and 42 controls were studied at center 1, 11 FTLD patients and 13 controls were studied at center 2. All PET images were intensity normalized to the cerebellum, primary sensorimotor cortex (SMC), cerebral global mean (CGM), and a reference cluster with most preserved FDG uptake in the aforementioned patients group of center 1. Metabolic deficits in the patient group at center 1 appeared 1.5, 3.6, and 4.6 times greater in spatial extent, when tracer uptake was normalized to the reference cluster rather than to the cerebellum, SMC, and CGM, respectively. Logistic regression analyses based on normalized values from FTLD-typical regions showed that at center 1, cerebellar, SMC, CGM, and cluster normalizations differentiated patients from controls with accuracies of 86%, 76%, 75% and 90%, respectively. A similar order of effects was found at center 2. Cluster normalization leads to a significant increase of statistical power in detecting early FTLD-associated metabolic deficits. The established FTLD-specific cluster can be used to improve detection of FTLD on a single case basis at independent centers - a decisive step towards early diagnosis and prediction of FTLD syndromes enabling specific therapies in the future.

Assessing psychosocial vulnerability and care needs of pretransplant patients by means of the INTERMED.

OBJECTIVE: We investigated whether the INTERMED, a generic instrument for assessing biopsychosocial case complexity and direct care, identifies organ transplant patients at risk of unfavourable post-transplant development by comparing it to the Transplant Evaluation Rating Scale (TERS), the established measure for pretransplant psychosocial evaluation. METHOD: One hundred nineteen kidney, liver, and heart transplant candidates were evaluated using the INTERMED, TERS, SF-36, EuroQol, Montgomery-Åsberg Depression Rating Scale (MADRS), and Hospital Anxiety & Depression Scale (HADS). RESULTS: We found significant relationships between the INTERMED and the TERS scores. The INTERMED highly correlated with the HADS,MADRS, and mental and physical health scores of the SF-36 Health Survey. CONCLUSIONS: The results demonstrate the validity and usefulness of the INTERMED instrument for pretransplant evaluation. Furthermore, our findings demonstrate the different qualities of INTERMED and TERS in clinical practice. The advantages of the psychiatric focus of the TERS and the biopsychosocial perspective of the INTERMED are discussed in the context of current literature on integrated care.

Localization and characterization of an active fault in an urbanized area in central Guatemala by means of geoelectrical imaging

The Polochic and Motagua faults define the active plate boundary between the North American and Caribbean plates in central Guatemala. A splay of the Polochic Fault traverses the rapidly growing city of San Miguel Uspantan that is periodically affected by destructive earthquakes. This fault splay was located using a 2D electrical resistivity tomography (ERT) survey that also characterized the fault damage zone and evaluated the thickness and nature of recent deposits upon which most of the city is built. ERT images show the fault as a similar to 50 m wide, near-vertical low-resistivity anomaly, bounded within a few meters by high resistivity anomalies. Forward modeling reproduces the key aspects of the observed electrical resistivity data with remarkable fidelity thus defining the overall location, geometry, and internal structure of the fault zone as well as the affected lithologies. Our results indicate that the city is constructed on a similar to 20 m thick surficial layer consisting of poorly consolidated, highly porous, water-logged pumice. This soft layer is likely to amplify seismic waves and to liquefy upon moderate to strong ground shaking. The electrical conductivity as well as the major element chemistry of the groundwater provides evidence to suggest that the local aquifer might, at least in part, be fed by water rising along the fault. Therefore, the potential threat posed by this fault splay may not be limited to its seismic activity per se, but could be compounded its potential propensity to enhance seismic site effects by injecting water into the soft surficial sediments. The results of this study provide the basis for a rigorous analysis of seismic hazard and sustainable development of San Miguel Uspantan and illustrate the potential of ERT surveying for paleoseismic studies.

Clustering in complex networks. II. Percolation properties

The percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows us to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the k-core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.

Tuning clustering in random networks with arbitrary degree distributions

We present a generator of random networks where both the degree-dependent clustering coefficient and the degree distribution are tunable. Following the same philosophy as in the configuration model, the degree distribution and the clustering coefficient for each class of nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding topologies by applying first a closure of triangles and second the classical closure of remaining free stubs. The procedure unveils an universal relation among clustering and degree-degree correlations for all networks, where the level of assortativity establishes an upper limit to the level of clustering. Maximum assortativity ensures no restriction on the decay of the clustering coefficient whereas disassortativity sets a stronger constraint on its behavior. Correlation measures in real networks are seen to observe this structural bound.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Influence of the pseudopotential on the properties of liquid rubidium at 315 K

The influence of the pseudopotential on both the structure and the self-diffusion of liquid rubidium at the melting point has been investigated by means of molecular-dynamics calculations. The model potential considered has been computed from the pseudopotential of Ashcroft, the dielectric function of Geldart and Vosko, and a Born-Mayer term. Four different values for the core radius which enters as input in the pseudopotential have been considered. In this way we have been able to observe and interpret the effect of this contribution on the properties of the liquid.

Weaver syndrome and EZH2 mutations: Clarifying the clinical phenotype.

Weaver syndrome, first described in 1974, is characterized by tall stature, a typical facial appearance, and variable intellectual disability. In 2011, mutations in the histone methyltransferase, EZH2, were shown to cause Weaver syndrome. To date, we have identified 48 individuals with EZH2 mutations. The mutations were primarily missense mutations occurring throughout the gene, with some clustering in the SET domain (12/48). Truncating mutations were uncommon (4/48) and only identified in the final exon, after the SET domain. Through analyses of clinical data and facial photographs of EZH2 mutation-positive individuals, we have shown that the facial features can be subtle and the clinical diagnosis of Weaver syndrome is thus challenging, especially in older individuals. However, tall stature is very common, reported in >90% of affected individuals. Intellectual disability is also common, present in ~80%, but is highly variable and frequently mild. Additional clinical features which may help in stratifying individuals to EZH2 mutation testing include camptodactyly, soft, doughy skin, umbilical hernia, and a low, hoarse cry. Considerable phenotypic overlap between Sotos and Weaver syndromes is also evident. The identification of an EZH2 mutation can therefore provide an objective means of confirming a subtle presentation of Weaver syndrome and/or distinguishing Weaver and Sotos syndromes. As mutation testing becomes increasingly accessible and larger numbers of EZH2 mutation-positive individuals are identified, knowledge of the clinical spectrum and prognostic implications of EZH2 mutations should improve.

Calculation of Activity Coefficients of Uni-Univalent Electrolytes by Equations Containing No Adjustable Parameters in Aqueous Solutions at 298.15 K

Normally either the Güntelberg or Davies equation is used to predict activity coefficients of electrolytes in dilute solutions when no better equation is available. The validity of these equations and, additionally, of the parameter-free equations used in the Bates-Guggenheim convention and in the Pitzerformalism for activity coefficients were tested with experimentally determined activity coefficients of HCl, HBr, HI, LiCl, NaCl, KCl, RbCl, CsCl, NH4Cl, LiBr,NaBr and KBr in aqueous solutions at 298.15 K. The experimental activity coefficients of these electrolytes can be usually reproduced within experimental errorby means of a two-parameter equation of the Hückel type. The best Hückel equations were also determined for all electrolytes considered. The data used in the calculations of this study cover almost all reliable galvanic cell results available in the literature for the electrolytes considered. The results of the calculations reveal that the parameter-free activity coefficient equations can only beused for very dilute electrolyte solutions in thermodynamic studies.

Oncogenic K-Ras segregates at spatially distinct plasma membrane signaling platforms according to its phosphorylation status

Activating mutations in the K-Ras small GTPase are extensively found in human tumors. Although these mutations induce the generation of a constitutively GTP-loaded, active form of K-Ras, phosphorylation at Ser181 within the C-terminal hypervariable region can modulate oncogenic K-Ras function without affecting the in vitro affinity for its effector Raf-1. In striking contrast, K-Ras phosphorylated at Ser181 shows increased interaction in cells with the active form of Raf-1 and with p110α, the catalytic subunit of PI 3-kinase. Because the majority of phosphorylated K-Ras is located at the plasma membrane, different localization within this membrane according to the phosphorylation status was explored. Density-gradient fractionation of the plasma membrane in the absence of detergents showed segregation of K-Ras mutants that carry a phosphomimetic or unphosphorylatable serine residue (S181D or S181A, respectively). Moreover, statistical analysis of immunoelectron microscopy showed that both phosphorylation mutants form distinct nanoclusters that do not overlap. Finally, induction of oncogenic K-Ras phosphorylation - by activation of protein kinase C (PKC) - increased its co-clustering with the phosphomimetic K-Ras mutant, whereas (when PKC is inhibited) non-phosphorylated oncogenic K-Ras clusters with the non-phosphorylatable K-Ras mutant. Most interestingly, PI 3-kinase (p110α) was found in phosphorylated K-Ras nanoclusters but not in non-phosphorylated K-Ras nanoclusters. In conclusion, our data provide - for the first time - evidence that PKC-dependent phosphorylation of oncogenic K-Ras induced its segregation in spatially distinct nanoclusters at the plasma membrane that, in turn, favor activation of Raf-1 and PI 3-kinase.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.